Various prior methods have been proposed for image dehazing. DCP [1]} is an effective strategy to restore the the transmission map using the statistical prior of the lowest pixel values in the color channels. Meng et al. [2]} estimates the transmission map by adding boundary constraint and contextual regularization. CAP [3]} applies the relationship between brightness and saturation to estimate the transmission map. IDE [4]} introduces a light absorption coefficient to the atmospheric scattering model. Wu et al. [5]} proposes an interleaved
cascade of shrinkage fields to reduce noise during recovering the transmission map and the scene radiance. Further, learning-based methods are becoming popular, thanks to the strong nonlinear capabilities of deep networks.
DehazeNet [6]}, MCSNN [7]} and DCPDN [8]} design CNNs to restore the haze-free images by using the atmospheric scattering model. Furthermore, AOD-Net [9]} applies a light-weight CNN to restore the clear images. GCANet[10]} leverages a gated-fusion subnetwork to fuse the middle features extracted from different convolutional layers. DuRN [11]} introduces a novel style of dual residual connection for image dehazing. GridNet [12]} proposes the attention-based multi-scale dehazing network. FD-GAN [13]} develops a fully end-to-end generative adversarial network with fusion discriminator for image dehazing. MSBDN [14]} designs a dense feature fusion module, which remedies the missing spatial information from high-resolution features.
RDN [15]} designs a deep retinex dehazing network, which jointly estimates the residual illumination map and the haze-free image.
Meanwhile, some methods propose to enrich the scene prior information of the input by various preprocessing operations. AMEF [16]} applies a multi-scale Laplacian pyramid to fuse a series of gamma correction operations, which compensates valid information with each other. GFN [17]} performs a gated network with fusing enhancement versions in three ways for preprocessing operations, including white balanced, contrast enhanced and gamma corrected. However, these methods ignore that real-world haze usually exhibits non-homogeneous distribution, so that well-preserved information could provide us with many valuable clues. However, convolution operations suffer from the limitation, which fails to explicitly utilize the information from the other distant regions, even they contain important scene prior.
